A fuzzy clustering approach to EP estimation

Journal of neural engineering, 2018

The extracellular action potentials recorded on an electrode result from the collective simultaneous electrophysiological activity of an unknown number of neurons. Identifying and assigning these action potentials to their firing neurons, "spike sorting", is an indispensable step in studying the function and the response of an individual or ensemble of neurons to certain stimuli. With the task of neural spike sorting, the determination of the number of clusters (neurons) is arguably the most difficult and the most challenging part due to the existence of background noise and the overlap and interactions among neurons in the neighbouring regions. It is not surprising that some researchers still rely on visual inspection by experts to estimate the number of clusters in neural spike sorting. Manual inspection, however, is not suitable to process the ever-growing vast amount of neural data. To address this pressing need, in this paper, thirty-three clustering validity indices ...

phD dissertation, 2005

In estimating auditory Evoked Potentials (EPs) from ongoing EEG the number of sweeps should be reduced to decrease the experimental time and to increase the reliability of diagnosis. The first goal of this study is to demonstrate the use of basic estimation techniques in extracting auditory EPs (AEPs) from small number of sweeps relative to ensemble averaging (EA). For this purpose, three groups of basic estimation techniques are compared to the traditional EA with respect to the signal-to-noise ratio(SNR) improvements in extracting the template AEP. Group A includes the combinations of the Subspace Method (SM) with the Wiener Filtering (WF) approaches (the conventional WF and coherence weighted WF (CWWF). Group B consists of standard adaptive algorithms (Least Mean Square (LMS), Recursive Least Square (RLS), and one-step Kalman filtering (KF). The regularization techniques (the Standard Tikhonov Regularization (STR) and the Subspace Regularization (SR) methods) forms Group C. All methods are tested in simulations and pseudo-simulations which are performed with white noise and EEG measurements, respectively. The same methods are also tested with experimental AEPs. Comparisons based on the output signal-to-noise ratio (SNR) show that: 1) the KF and STR methods are the best methods among the algorithms tested in this study,2) the SM can reduce the large amount of the background EEG noise from the raw data, 3) the LMS and WF algorithms show poor performance compared to EA. The SM should be used as a pre-filter to increase their performance. 4) the CWWF works better than the WF when it is combined with the SM, 5) the STR method is better than the SR method. It is observed that, most of the basic estimation techniques show definitely better performance compared to EA in extracting the EPs. The KF or the STR effectively reduce the experimental time (to one-fourth of that required by EA). The SM is a useful pre-filter to significantly reduce the noise on the raw data. The KF and STR are shown to be computationally inexpensive tools to extract the template AEPs and should be used instead of EA. They provide a clear template AEP for various analysis methods. To reduce the noise level on single sweeps, the SM can be used as a pre-filter before various single sweep analysis methods. The second goal of this study is to to present a new approach to extract single sweep AEPs without using a template signal. The SM and a modified scale-space filter (MSSF) are applied consecutively. The SM is applied to raw data to increase the SNR. The less-noisy sweeps are then individually filtered with the MSSF. This new approach is assessed in both pseudosimulations and experimental studies. The MSSF is also applied to actual auditory brainstem response (ABR) data to obtain a clear ABR from a relatively small number of sweeps. The wavelet transform coefficients (WTCs) corresponding to the signal and noise become distinguishable after the SM. The MSSF is an effective filter in selecting the WTCs of the noise. The estimated single sweep EPs highly resemble the grand average EP although less number of sweeps are evaluated. Small amplitude variations are observed among the estimations. The MSSF applied to EA of 50 sweeps yields an ABR that best fits to the grand average of 250 sweeps. We concluded that the combination of SM and MSSF is an efficient tool to obtain clear single sweep AEPs. The MSSF reduces the recording time to one-fifth of that required by EA in template ABR estimation. The proposed approach does not use a template signal (which is generally obtained using the average of small number of sweeps). It provides unprecedented results that support the basic assumptions in the additive signal model. Keywords: Auditory Evoked Potential, Adaptive filtering, Tikhonov regularization,Wavelet Transform

Pattern Recognition Techniques, Technology and Applications, 2008

Analysis of electrophysiological brain activity has long been considered as one of indispensable tools enabling clinicians and scientists to investigate various aspects of cognitive brain functionality and its underlying neurophysiological structure. The relevance of electroencephalogram (EEG) in particular, due to its inexpensive and most importantly, non-invasive acquisition procedure, has been reflected in the abundance of clinical applications and the diversity of areas of research studies it has contributed to. These studies lie within the realm of brain science understood nowadays in a broad sense embracing and linking interdisciplinary fields of neuroimaging, cognitive psychology and neurophysiology among others. In medical practice, EEG is used more pragmatically to support clinicians in their effort to establish the presence, severity and cerebral distribution of neurological disorders. Epilepsy diagnostic serves as a prime example in this regard . The complex nature of brain signals and the intricacies of the measurement process involved , particularly in the case of EEG, render their analysis and interpretation challenging . Historically, these signals used to be examined only qualitatively based on routine visual inspection and the experience of responsible technicians or practitioners. With the advent of the era of digital biosignal recordings, computerised quantitative electroencephalography gained notable popularity as a supplementary tool enhancing objectiveness of analysis . The fast pace of technological advancement, considerable progress in neuroscience and neuroengineering along with growing investments in medical and health sectors among others have opened up new possibilities for automated EEG analysis systems. A continually growing scope for their applications set dominant design trends and imposed requirements regarding their functionality that prevail in today's practice and research. One of the key points in this regard is the need for the increased independence, autonomy and thus the improved reliability of such systems. This has led to a more comprehensive formulation of a computational problem of brain signal analysis within the realm of pattern recognition, which facilitates a more generic description of existing approaches, and development or reuse of suitable pattern recognition methods. In consequence, the notion of brain signal pattern recognition has been introduced to refer to the underlying concept of processing raw data with the aim of acting upon its category . The objective is to identify patterns in electrophysiological brain activity that are

Computers in Biology and Medicine, 2006

The aim of this paper consists in highlighting the use of the averaging technique in some biomedical applications, such as evoked potentials (EP) extraction. We show that this technique, which is generally considered as classical, can be very efficient if the dynamic model of the signal ...

Behavioural Brain Research, 2017

Researchers often rely on simple methods to identify involvement of neurons in a particular motor task. The historical approach has been to inspect large groups of neurons and subjectively separate neurons into groups based on the expertise of the investigator. In cases where neuron populations are small it is reasonable to inspect these neuronal recordings and their firing rates carefully to avoid data omissions. In this paper, a new methodology is presented for automatic objective classification of neurons recorded in association with behavioral tasks into groups. By identifying characteristics of neurons in a particular group, the investigator can then identify functional classes of neurons based on their relationship to the task. The methodology is based on integration of a multiple signal classification (MUSIC) algorithm to extract relevant features from the firing rate and an expectation-maximization Gaussian mixture algorithm (EM-GMM) to cluster the extracted features. The methodology is capable of identifying and clustering similar firing rate profiles automatically based on specific signal features. An empirical wavelet transform (EWT) was used to validate the features found in the MUSIC pseudospectrum and the resulting signal features captured by the methodology. Additionally, this methodology was used to inspect behavioral elements of neurons to physiologically validate the model. This methodology was tested using a set of data collected from awake behaving non-human primates.